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Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-8929-7220
2018 (English)In: Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018 / [ed] Brynielsson, J, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 77-80Conference paper, Published paper (Refereed)
Abstract [en]

Law enforcement agencies strive to link crimes perpetrated by the same offenders into crime series in order to improve investigation efficiency. Such crime linkage can be done using both physical traces (e.g., DNA or fingerprints) or 'soft evidence' in the form of offenders' modus operandi (MO), i.e. their behaviors during crimes. However, physical traces are only present for a fraction of crimes, unlike behavioral evidence. This work-in-progress paper presents a method for aggregating multiple criminal profilers' ratings of offenders' behavioral characteristics based on feature-rich crime scene descriptions. The method calculates consensus ratings from individual experts' ratings, which then are used as a basis for classification algorithms. The classification algorithms can automatically generalize offenders' behavioral characteristics from cues in the crime scene data. Models trained on the consensus rating are evaluated against models trained on individual profiler's ratings. Thus, whether the consensus model shows improved performance over individual models. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 77-80
Keywords [en]
classification, crime linkage, Multi-expert decision making, offender profiling, Classification (of information), Risk management, Risk perception, Behavioral characteristics, Classification algorithm, Consensus models, Individual models, Law-enforcement agencies, Work in progress, Crime
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18621DOI: 10.1109/EISIC.2018.00021ISI: 000483031300012Scopus ID: 2-s2.0-85069498311ISBN: 9781538694008 (print)OAI: oai:DiVA.org:bth-18621DiVA, id: diva2:1349904
Conference
8th European Intelligence and Security Informatics Conference, EISIC, Karlskrona, 24 October 2018 through 25 October 2018
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-19Bibliographically approved

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Boldt, MartinBoeva, VeselkaBorg, Anton

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